Standardised Training Program in Spinal Ultrasound for Epidural Insertion: Protocol-Driven versus Non—Protocol Driven Teaching Approach
Bibliographic record
Abstract
Spinal ultrasonography provides guidance for epidural insertion in obstetric patients. The primary objective of the study was to develop a training program in spinal ultrasound for anaesthetists and to determine its effect on the skill acquisition of anaesthetists with no prior spinal ultrasound experience. Eighteen anaesthetists underwent two structured workshops (one week apart), each followed by a practice session and videorecorded assessments. Participants were randomised to a protocol-driven or non-protocol driven spinal ultrasound teaching program. Two experts rated each individual's performance using a global rating scale (GRS), checklist and image quality scale. The primary outcome was the mean difference in GRS score between the two workshops, analysed using linear mixed models. Intraclass correlation coefficients were calculated to assess agreement between assessors' ratings. A total of 108 ultrasound scans were performed on five pregnant volunteers during the assessment periods. After adjusting for confounders, GRS scores increased on all three rating scales at the second workshop, this increase being 6.01 points (95% confidence interval 4.56 to 7.46, P<0.001) from a mean score of 28.4 (95% confidence interval 24.8 to 32.0). There was no significant difference in the scores between the two teaching groups (difference in GRS scores=1.36 points, 95% confidence interval -0.77 to 3.50, P=0.211). Intraclass correlation coefficients showed substantial assessor agreement for all three assessment methods (range 0.59 to 0.89). The results demonstrate that programmed spinal ultrasound training sessions involving practice with guidance and feedback from an expert, whether protocol-based or non-protocol based, lead to improved performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".